Upload HuggingFaceTB_SmolLM3-3B_0.py with huggingface_hub

HuggingFaceTB_SmolLM3-3B_0.py

@@ -21,6 +21,95 @@ try:
     tokenizer = AutoTokenizer.from_pretrained(model_id)
     
     pipe = pipeline("text-generation", model=model_id, tokenizer=tokenizer)
+    
+    messages = [
+        {"role": "user", "content": "Give me a brief explanation of gravity in simple terms."},
+    ]
+    pipe(messages)
+    
+    messages = [
+        {"role": "system", "content": "/no_think"},
+        {"role": "user", "content": "Give me a brief explanation of gravity in simple terms."},
+    ]
+    pipe(messages)
+    
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    
+    model_name = "HuggingFaceTB/SmolLM3-3B"
+    device = "cuda" # for GPU usage or "cpu" for CPU usage
+    
+    # load the tokenizer and the model
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+    ).to(device)
+    
+    # prepare the model input
+    prompt = "Give me a brief explanation of gravity in simple terms."
+    messages_think = [
+        {"role": "user", "content": prompt}
+    ]
+    
+    text = tokenizer.apply_chat_template(
+        messages_think,
+        tokenize=False,
+        add_generation_prompt=True,
+    )
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    
+    # Generate the output
+    generated_ids = model.generate(**model_inputs, max_new_tokens=32768)
+    
+    # Get and decode the output
+    output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :]
+    print(tokenizer.decode(output_ids, skip_special_tokens=True))
+    
+    prompt = "Give me a brief explanation of gravity in simple terms."
+    messages = [
+        {"role": "system", "content": "/no_think"},
+        {"role": "user", "content": prompt}
+    ]
+    
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+    )
+    
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    
+    # Generate the output
+    generated_ids = model.generate(**model_inputs, max_new_tokens=32768)
+    
+    # Get and decode the output
+    output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :]
+    print(tokenizer.decode(output_ids, skip_special_tokens=True))
+    
+    tools = [
+        {
+            "name": "get_weather",
+            "description": "Get the weather in a city",
+            "parameters": {"type": "object", "properties": {"city": {"type": "string", "description": "The city to get the weather for"}}}}
+    ]
+    
+    messages = [
+        {
+            "role": "user",
+            "content": "Hello! How is the weather today in Copenhagen?"
+        }
+    ]
+    
+    inputs = tokenizer.apply_chat_template(
+        messages,
+        enable_thinking=False, # True works as well, your choice!
+        xml_tools=tools,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_tensors="pt"
+    ).to(model.device)
+    
+    outputs = model.generate(inputs)
+    print(tokenizer.decode(outputs[0]))
     with open('HuggingFaceTB_SmolLM3-3B_0.txt', 'w') as f:
         f.write('Everything was good in HuggingFaceTB_SmolLM3-3B_0.txt')
 except Exception as e: